class_docstr: Result of fitting a discrete or continuous distribution to data
  
  Attributes
  ----------
  params : namedtuple
      A namedtuple containing the maximum likelihood estimates of the
      shape parameters, location, and (if applicable) scale of the
      distribution.
  success : bool or None
      Whether the optimizer considered the optimization to terminate
      successfully or not.
  message : str or None
      Any status message provided by the optimizer.
docstr: Negative log-likelihood function
  
  Evaluates the negative of the log-likelihood function of the provided
  data at the provided parameters.
  
  Parameters
  ----------
  params : tuple, optional
      The shape parameters, location, and (if applicable) scale of the
      distribution as a single tuple. Default is the maximum likelihood
      estimates (``self.params``).
  data : array_like, optional
      The data for which the log-likelihood function is to be evaluated.
      Default is the data to which the distribution was fit.
  
  Returns
  -------
  nllf : float
      The negative of the log-likelihood function.
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code: def nllf(self, params=None, data=None):
      """Negative log-likelihood function
  
      Evaluates the negative of the log-likelihood function of the provided
      data at the provided parameters.
  
      Parameters
      ----------
      params : tuple, optional
          The shape parameters, location, and (if applicable) scale of the
          distribution as a single tuple. Default is the maximum likelihood
          estimates (``self.params``).
      data : array_like, optional
          The data for which the log-likelihood function is to be evaluated.
          Default is the data to which the distribution was fit.
  
      Returns
      -------
      nllf : float
          The negative of the log-likelihood function.
  
      """
      params = params if params is not None else self.params
      data = data if data is not None else self._data
      return self._dist.nnlf(theta=params, x=data)
  
imports: import warnings
  from collections import namedtuple
  import numpy
  from scipy import optimize, stats
  from scipy._lib._util import check_random_state
label_desc: Use image node_id 3 for calling the FitResult obj's underlying member method code with example usage: obj.nllf(params, data) and returns: self